The association between weight-adjusted waist index and sleep disorders in U.S. adults: results from NHANES 2005–2008

The detrimental effects of obesity on sleep disorders have garnered a lot of interest. The weight-adjusted waist index (WWI) is a newly developed anthropometric index calculated in terms of weight and waist circumference. The body mass index has been employed to evaluate obesity in the majority of studies that connect obesity to sleep disorders. This study seeks to investigate the correlation between WWI and sleep disorders among adults in the United States. This cross-sectional study was part of the National Health and Nutrition Examination Survey and included adults aged >20 from 2005 to 2008. This study investigated the linear relationship between sleep disorders and WWI using weighted binary logistic regression models. Nonlinear relationships were characterized using smooth curve fitting and threshold effects analyses. After that, based on variables like gender, age, marital status, diabetes, hypertension, and smoking, subgroup analyses were performed. Our study included 9869 participants who were at least 20 years old. Higher WWI was linked to greater odds of sleep disorders prevalence, according to weighted binary logistic regression (odds ratio = 1.15; 95% confidence interval, 1.10, 1.20). In subgroup analyses based on age, marital status, diabetes, hypertension, and smoking, this connection remained robust. However, there were notable differences in this connection depending on gender. Furthermore, a nonlinear correlation with inflection points between WWI and sleep disorders was shown using smooth curve fitting. The nonlinear association between WWI and sleep disorders has an inflection point of 8.1 cm/√kg, as indicated by the threshold effect analyses. A higher WWI exposure may elevate the odds of sleep disorder prevalence, underscoring the importance of considering WWI in the prevention and management of sleep disorders.


Introduction
Sleep is essential for all human activities.About one-third of a person's life is spent sleeping.Numerous behavioral and physiological systems depend on sleep, and when these processes are disrupted, it can result in a variety of sleep disorders and eventually physiological dysfunction. [1]The Centers for Disease Control classifies sleep disorders as a public health issue. [2]In the general population, sleep disorders are strongly linked to higher death rates, metabolic diseases, atherosclerosis, cardiovascular disease, and lower standards of life. [3,4]besity is becoming more commonplace worldwide, and being overweight or obese has become a major public health concern. [5]A body mass index (BMI) of more than 25 kg/m 2 is classified as overweight and more than 30 kg/m 2 is classified as obese. [6][9] Furthermore, obesity negatively affects sleep due to the development of obstructive sleep apnea, obesity hypoventilation syndrome, and the release of inflammatory cytokines that interfere with sleep cycles. [10,11]MI has been used to evaluate obesity in previous research.However, because it ignores variations in bone density, fat distribution, and muscle mass, its validity as a crude indicator of obesity has been called into question. [12]Waist circumference (WC), on the other hand, has a substantial link with the accumulation of abdominal fat, making it a stronger indication of the health The authors have no funding and conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are publicly available.
The NCHS Ethics Review Board evaluated and gave its approval to this project.The requirements of local legislation and institutions were followed in the performance of this investigation.To participate in the study, participants had to give written informed consent.Since the NHANES database is accessible to the general public, institutional review board approval was not necessary.a The First Clinical Medical School, Guangzhou Medical University, Guangzhou, China.
effects of obesity. [13]The weight-adjusted waist index (WWI), which is computed by dividing WC (cm) by the square root of body weight (kg) (cm/√kg), is a novel obesity index that researchers have just presented. [14]The index represents weight-independent central obesity while accounting for body weight. [15]It is a more reliable indicator of cardiovascular disease morbidity and mortality than BMI, body shape index, and waist-to-height ratio, according to earlier research. [14,16]Additionally, WC and BMI were not as good predictors of hypertension as WWI was. [17][23] There is little research on the connection between WWI and sleep disorders, even though WWI can be used as a marker of central obesity.Consequently, to deepen our understanding of this field, we evaluated the relationship between WWI and sleep disorders in individuals whose sleep disorders were investigated.

Study population
The National Center for Health Statistics conducts National Health and Nutrition Examination Survey (NHANES) research to investigate the health and nutritional status of the US population.All NHANES data is publicly accessible at https://www.cdc.gov/nchs/nhanes/.The NHANES sample is representative due to the stratified multistage probability sampling method employed in the research design.Data for this cross-

Exposure and ending definition
WWI (cm/√kg) was the exposure variable in this study.The presence or absence of a sleep disorder was the targeted independent variable.Sleep disorders were covered by NHANES questions SLQ050 and SLQ060, which asked: "Ever told by the doctor have a sleep disorder?,""Ever told doctor had trouble sleeping?"The response of "yes" indicated the presence of a sleep disorder.SLQ070: Self-reporting of insomnia, restless legs, sleep apnea, or other sleep disorders.Following their response, those who replied "yes" were then deemed to have a sleep disorder. [24]

Covariates
Based on reports in the literature, [25][26][27][28][29][30] covariates that could potentially influence the link between WWI and sleep disorders were integrated into our survey design.The age and ratio of poverty-to-income ratio were continuous variables.Gender, race, education level, marital status, hypertension, diabetes, smoking, and alcohol consumption were categorical variables.Individuals are classified as smokers if they smoke more than 100 cigarettes in their lifetime, and drinkers if they drink more than 12 times a year in their lifetime.Hypertension and diabetes are defined based on participants' responses to the questions, "Has a doctor ever told you that you have hypertension?"and "Has a doctor ever told you that you have diabetes?"respectively.These factors were all taken from NHANES questionnaire data and demographic data.The public can see all their detailed measurement protocols at https://www.cdc.gov/nchs/nhanes/.

Statistical analyses
Following the guidelines outlined by the Centers for Disease Control and Prevention, we conducted all statistical analyses utilizing R (http://www.r-project.org) and EmpowerStats (http://www.empowerstats.com).The threshold for statistical significance is set at P < .05.This study applied the National Center for Health Statistics analytic guidelines to generate estimates and used sample weights aligned with NHANES objectives, ensuring the study's representativeness.Categorical variables were expressed as percentages, while continuous data were presented as means and standard deviations.To evaluate differences across WWI triads, weighted Student's t tests were used for normally distributed continuous variables, Kruskal-Wallis test for nonnormally distributed continuous variables, and weighted chi-square tests for categorical variables.The study utilized weighted multiple logistic regression to investigate the independent association between WWI and sleep disorders across 3 models.To explore the nonlinear relationship, smooth curve fitting and threshold effects analyses were conducted, complemented by subgroup analyses that considered gender, age, marital status, diabetes, hypertension, and smoking.

Baseline characteristics of the study population
There were 9869 individuals in this study aged at least 20 years.The participants' average age was 49.04 ± 18.16 years, with 51.56% of them were female and 48.44% of them were male.The majority of the population were White individuals, with 48.12%, followed by Non-Hispanic Black individuals (21.52%),Mexican Americans (18.72%),Other Hispanic individuals (7.57%), and other races (4.07%).The average WWI was 8.09 ± 1.47 cm/√kg.24.11% experienced problems with their sleep.Table 1 lists the individuals' clinical characteristics, and all individuals were evenly separated into 3 groups based on WWI using column-stratified grouping.The tertiles are demarcated as follows: Tertile 1 ranges from 4.11 to 7.37, Tertile 2 from 7.37 to 8.58, and Tertile 3 from 8.58 to 17.65.Compared to Tertile 1, the highest WWI tertile (Tertile 3) was associated with greater rates of diabetes and hypertension, as well as being male, non-Hispanic White, and having a higher family income level.

The occurrence of sleep disorders is correlated with WWI
Table 2 indicates that WWI and sleep disorders are positively correlated.The association remained unchanged in the fully adjusted model (Model 3) (odds ratio [OR] = 1.15; 95% confidence interval [CI] 1.10, 1.20).According to the study, the odds of suffering from sleep disorders increased by 15% for every unit increase in WWI.We also transformed WWI into a tri-categorical variable from a continuous one.Tertile 3 had odds of sleep disorder prevalence that were 37% greater than those of Tertile 1, which was found in the fully adjusted model (OR = 1.37; 95% CI 1.12, 1.68).However, Tertile 2 did not have any statistically significant variation compared to Tertile 1 (OR = 1.03; 95% CI 0.83, 1.28).

Smooth curve fitting
The non-linear relationship between WWI and sleep disorders was studied through the use of smooth curve fitting.In Figure 2, the positive non-linear relationship between WWI and sleep disorders is demonstrated.

Analyses of the threshold effect of WWI on the odds of sleep disorders prevalence
We found that there was an inflection point of 8.1 cm/√kg for sleep disorders by an investigation of the threshold influence of WWI on sleep disorders (Table 3).To the right of the inflection point, there was a positive correlation between WWI and sleep disorders (OR = 1.24; 95% CI 1.18, 1.31), while there was no statistically significant correlation to the left of the inflection point (OR = 1.03; 95% CI 0.96, 1.11).To put it another way, there was not a significant connection between WWI and sleep disorders until WWI reached 8.1 cm/ kg.Sleep disorders were found to be strongly correlated with an increase in WWI once it reached 8.1 cm/kg.For every unit rise in WWI, there was a 24% increase in the odds of sleep disorders prevalence.

Subgroup analyses
Stratified by gender, age, marital status, diabetes, hypertension, and smoking, subgroup analyses, and interaction tests were carried out to assess the robustness of the association between WWI and sleep disorders and to pinpoint possible population variances (Table 4).The subgroup analyses revealed that there was a significant correlation (all P < .05) between WWI and greater odds of sleep disorder prevalence in each of the subgroups stratified.Significant gender-based variations were found in the interaction tests examining the relationship between WWI and sleep disorders.As WWI increased, the odds of sleep disorder prevalence were higher in the male group than in the female group.There was no significant difference between the rest of the strata, which suggests that age, marital status, diabetes, hypertension, and smoking were stable in the relationship between WWI and sleep disorders.

Discussion
This study aimed to investigate the relationship between WWI and sleep disorders among US adults.Our cross-sectional survey of 9869 adults revealed a correlation between greater levels of WWI and sleep disorders.Investigated from the inflection point's left and right sides (WWI = 8.1 cm/√kg), the right side showed a significant relationship with the odds of sleep disorders prevalence, but the left side did not show a statistically significant relationship.The findings of this study imply that lowering WWI levels may help lower the prevalence of sleep disorders.Research has now demonstrated that obesity is a significant risk factor and a predictor of unfavorable clinical outcomes. [31]][34] BMI is currently the accepted measure for screening for obesity.However, because BMI measures extra weight rather than body fat, it only partially represents obesity.BMI does not take into account the adipose tissue distribution of major central fat deposits around the neck, trunk, and abdominal viscera, but the regional distribution of body fat as measured by waist and neck circumference is a better determinant of the severity of OSA than is generalized obesity. [35]MI is an insufficient biomarker of abdominal obesity, however, WC is a straightforward approach to measuring abdominal obesity that can be standardized and applied clinically. [15]erefore, our team decided to use the WWI index-which is derived from weight and WC-as the screening indication given the significance of the completeness of the evaluation of obesity.
This study is the first to focus on the association between WWI and sleep disorders.Previous research has shown that obesity has a negative impact on sleep quality, including obesity hypoventilation syndrome and OSA. [10]Obesity raises the risk of OSAS, which is probably caused by excess adipose tissue constricting the upper airway. [36]Obesity also causes abnormal ventilation-perfusion ratios, reduced lung capacity, and restricted lung and chest wall motion. [36]Sleep apnea resulting from OSAS can produce intermittent hypoxia and hypercapnia, which can cause sleep disorders such as fragmented sleep, recurrent nocturnal arousals, and enhanced respiratory effort. [37]Obesity enlarges the amount and size of pharyngeal fat deposits, which is a major contributor to respiratory compression.The development of OSAS may be influenced by fat deposits in the upper airway and the area around the thoracic cavity. [38]Furthermore, elevated visceral adipose tissue may secrete inflammatory cytokines including TNF-α, IL-6, and IL-1, which might interfere with sleep-wake cycles. [39,40]The 2 most researched cytokines linked to obesity are TNF-a and IL-6, which are found in higher concentrations in the adipose tissue and serum of obese individuals. [41]Research has demonstrated heightened somnolence in obese participants without sleep disorders.This could be attributed to the impact of IL-6 generated by obesity-related adiposity. [42]A clinical study's findings indicated that obese individuals who had shed pounds had decreased levels of TNF-a and IL-6 as well as improved sleep duration and quality. [43]In one study, weight loss and lifestyle modifications led to sustained improvements in the severity of OSA and associated comorbidities.Spanish men who were overweight or obese and received CPAP treatment for moderate to severe OSA demonstrated improvement in both their hepatic and cardiovascular functioning, respectively. [44]In a different study, throughout the very low-calorie ketogenic diet-induced weight reduction, improvements in sleep indices were noted, particularly during the maximum loss of adiposity. [45]According to a study by Shade et al, women who lost 5 percent or more of their body weight experienced improvements in blood pressure, pain interference, and sleep quality.Weight reduction also lessens discomfort and sleep disturbance, and women who experience fewer sleep disruptions may also have lower blood pressure. [46]Furthermore, the research has demonstrated a significant correlation between amount of sleep and WC, suggesting that sleep deprivation and increased central adiposity can co-occur. [47]A 1-centimeter increase in WC represents a percentage of increased risk for mild, moderate, and severe OSA. [48]owever, as academic research progressed using BMI and WC as indicators of obesity, the obesity paradox emerged.According to research by Rahman et al, [49] calcification of the abdominal aorta was negatively correlated with an increase in BMI.According to Park et al, [14] survivors all had mean BMI that were unexpectedly higher than those of decedents who died from cardiovascular or all-cause mortality.The obesity paradox has brought into question the validity of BMI as an indicator of obesity in academia.WWI is a relatively new measurement tool, and recent studies have shown that it can distinguish between muscle mass and adiposity.[51][52][53][54] The main advantages of this study are that it is the initial cross-sectional analyses to examine the connection between WWI and sleep disorders, and it has a sizable and representative sample size.The findings are broadly applicable to the entire US population because we used national data and took sample weights into account in our study.Covariate adjustments were made to the regression models, and robustness was confirmed by subgroup analyses made possible by the high sample size.Furthermore, WWI is capable of analyzing both muscle and fat mass and is not restricted to analyzing fat content in specific regions.Instead, it considers visceral fat and WC, yielding more complete findings.However, there are several shortcomings in our study.Firstly, in order to ascertain if a causal association exists between WWI and sleep disorders, more study is necessary as the cross-sectional survey was unable to determine it.Secondly, there was a dearth of externally validated sleep disorders in the study, such as the PSQI scale, the ESS scale, and the DBAS scale.A memory bias was introduced because the diagnosis of sleep disorders was solely based on participant self-reports.Thirdly, several possible confounders may have had an impact on WWI and sleep disorders.Even with the inclusion of quite a few pertinent factors in our model, it is still difficult to eliminate the impact of additional possible confounding variables.Fourthly, as our research is limited to a single nation and ethnic group, it is necessary to determine whether the results apply to other nations or ethnic groups.

Conclusion
According to our research, a higher level of WWI was linked to larger odds of sleep disorder prevalence.Thus, high WWI may be a potential risk for sleep disorders.To avoid jeopardizing their health, those with greater WWI should pay more attention to their sleep health.However, further large-scale prospective investigations are required to bolster the findings presented here.
sectional study was contributed by NHANES participants aged over twenty who participated in the 2005-2008 cycle.Throughout the 2005-2006 and 2007-2008 periods, 20,498 people took part in NHANES, with no data on body weight (n = 1020), WC (n = 2282), sleep disorders (n = 5671), and those under the age of 20 (n = 1656) excluded (Fig. 1).

Figure 1 .
Figure 1.Flowchart showing the NHANES 2005-2008 sample selection process.NHANES = National Health and Nutrition Examination Survey.

Figure 2 .
Figure 2. The smooth curve fitting analyses of WWI and sleep disorders.The solid red line is the representation of the smooth curve between the variables.The blue band represents the results, which are 95% confidence intervals adjusted for gender, age, race, marital status, education level, household income to poverty ratio, smoking, alcohol use, diabetes, and hypertension.WWI = weight-adjusted waist index.

Table 1
The research population's baseline characteristics based on the weight-adjusted waist index tertile.
Mean + SD for normally distributed continuous variables.Med (IQR) for non-normally distributed continuous variables; P value for normally distributed continuous variables was calculated by subsequent students' t test; P value for non-normally distributed continuous variables was calculated by the Kruskal-Wallis test.% for Categorical variables: P value was calculated by the weighted chi-square test.

Table 2
Association between WWI and sleep disorder.

Table 3
Analyses of the threshold effect of WWI on the odds of sleep disorder prevalence.Adjusting for gender, age, race, education level, PIR, marital status, hypertension, diabetes, smoking, and alcohol intake ≥ 12 drinks/yr, the results of the threshold effect of WWI on the odds of sleep disorders prevalence were obtained.PIR = poverty-to-income ratio, WWI = weight-adjusted waist index.